Packages and data

library(tidyverse)
library(patchwork)

tcga_clinical <- read_delim(here::here("data", "tcga_clinical.txt"))

head(tcga_clinical)

Visualizing associations among two or more variables

Scatterplot

Relationship between height vs weight

# set theme to theme_bw()
theme_set(theme_bw(base_size = 15))

wt_ht_scatter <- ggplot(
  data = tcga_clinical,
  mapping = aes(x = weight, y = height)) +
  geom_point()

wt_ht_scatter

Mapping color to gender variable from the data

wt_ht_scatter_gender <- ggplot(
  data = tcga_clinical,
  mapping = aes(x = weight, y = height, color = gender)) +
  geom_point()

wt_ht_scatter_gender

Avoid overplotting using jitter plot

tcga_clinical %>% 
  ggplot(mapping = aes(x = stage, y = height)) +
  geom_point() +
  geom_jitter(aes(color = stage), width = 0.2, size = 2, alpha = 0.7) +
  labs(x = NULL, y = "Height (cm)") +
  scale_color_brewer(NULL, palette = "Paired")

Note: geom_jitter is a shortcut for geom_point(position = "jitter")

Line plot

Line plot of mean age by Karnofsky Performance Score for each stage

# mean age
mean_age_kps <- tcga_clinical %>% 
  drop_na(stage, karnofsky_performance_score) %>% 
  group_by(stage, karnofsky_performance_score) %>% 
  summarise(mean_age = mean(age_at_initial_pathologic_diagnosis, na.rm = TRUE))
## `summarise()` has grouped output by 'stage'. You can override using the
## `.groups` argument.
# plot
mean_age_kps_line <- mean_age_kps %>% 
  ggplot(aes(x = factor(karnofsky_performance_score), y = mean_age, 
             group = stage, color = stage)) +
  geom_point(size = 2) +
  geom_line() +
  scale_x_discrete("Karnofsky Performance Score") +
  scale_y_continuous("Mean age at diagnosis") +
  scale_color_brewer("Stage", palette = "Paired") 

mean_age_kps_line

Visualizing amounts

We might be interested in visualizing amounts i.e. numerical values for some set of categories

Bar plot

tcga_clinical %>% 
  ggplot(aes(x = stage)) +
  geom_bar(fill = "gray60")

You could also calculate counts in advance and plot it but you need to use stat = "identity"

# Count by stage
stage_count <- tcga_clinical %>% 
  count(stage, .drop = TRUE)

# use stat = "identity"
ggplot(data = stage_count, aes(x = stage, y = n)) +
  geom_bar(stat = "identity", fill = "gray60") +
  labs(x = "Stage", y = "Number of patients")

Or, just use geom_col()

ggplot(data = stage_count, aes(x = stage, y = n)) +
  geom_col(fill = "gray60") +
  labs(x = "Stage", y = "Number of patients")

Sometimes x-axis labels identifying each bar take up a lot of horizontal space

ggplot(data = stage_count, aes(y = fct_reorder(stage, n), x = n)) +
  geom_bar(stat = "identity", fill = "gray60") +
  labs(x = "Stage", y = NULL)

Grouped bar plot

groupedbar_gender <- tcga_clinical %>% 
  ggplot(aes(x = stage, fill = gender)) +
  geom_bar(position = "dodge") +
  labs(x = NULL, y = "Number of patients") +
  scale_fill_viridis_d(NULL)

groupedbar_gender

Stacked bar plot

stackedbar_gender <- tcga_clinical %>% 
  ggplot(aes(x = stage, fill = gender)) +
  geom_bar(position = "stack") +
  labs(x = NULL, y = "Number of patients") +
  scale_fill_viridis_d(NULL)

stackedbar_gender

Visualizing distributions

To understand how a particular variable is distributed in a dataset

Boxplot

boxplot_gender <- ggplot(
  data = tcga_clinical, 
  aes(x = stage, y = weight, fill = gender)) +
  geom_boxplot() +
  labs(x = NULL, y = "Weight (kg)") +
  scale_fill_brewer(palette = "Dark2")

boxplot_gender

histogram and density plot

Histogram count plot for single distribution

hist_age <- ggplot(tcga_clinical, aes(x = age_at_initial_pathologic_diagnosis)) +
  geom_histogram(fill = "gray60", color = "gray80") +
  labs(x = "Age at diagnosis")

hist_age
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Density plot for visualizing multiple distributions at the same time

ggplot(
  tcga_clinical, 
  aes(x = height, fill = gender)) +
  geom_density(alpha = 0.8, color = "transparent") +
  labs(x = "Height (cm)") +
  scale_fill_viridis_d(NULL)

Violin plot

Violin plot is the combination of the box plot with a kernel density plot.

wt_violin <- ggplot(
  tcga_clinical, aes(x = gender, y = weight)) +
  geom_violin(trim = FALSE) 

wt_violin

Quantiles can be added the plot by passing a vector to the draw_quantiles argument

# violin plot with quantile lines
ggplot(
  tcga_clinical, aes(x = gender, y = weight)) +
  geom_violin(trim = FALSE, draw_quantiles = c(0.25, 0.5, 0.75))

We can also overlay box plot to the violin plot

wt_violin2 <- ggplot(
  tcga_clinical, aes(x = gender, y = weight, fill = gender)) +
  geom_violin(trim = FALSE) +
  geom_boxplot(width = 0.1, show.legend = FALSE)

wt_violin2

Heatmaps

wt_heatmap <- ggplot(tcga_clinical, aes(x = stage, y = tumor_tissue_site, fill = weight)) +
  geom_tile(
    color = "white", lwd = 1, linetype = 1) +
  scale_fill_gradient(
    low = "white", high = "blue", na.value = "red") +
  coord_fixed() + 
  theme_minimal(base_size = 15) +
  theme(panel.grid = element_blank(),
        axis.title = element_blank())

wt_heatmap

Combining plots

{patchwork} R package has made combining multiple plots ridiculously simple

Horizontal layout

# combine two plots horizontally
wt_ht_scatter + mean_age_kps_line

or you can use “|” too

Vertical layout

# stack two plots
wt_violin2 / hist_age

Nested layout

nested_patch <- mean_age_kps_line / (wt_ht_scatter | hist_age)

nested_patch

Be careful with the operator precedence rule (e.g. / is evaluated before |)

{patchwork} provides wrap_plots() for a more functional approach to assembly

Annotating plots

(boxplot_gender / (wt_ht_scatter | hist_age)) +
  plot_annotation(
    title = "Combining three plots",
    subtitle = "Annotating each plot",
    caption = "Data source: TCGA data",
    tag_levels = "A", tag_suffix = ".")

Modifying patches

Use & to add elements to all subplots in the patchwork

mean_age_kps_line / (wt_ht_scatter | hist_age) & theme_minimal()

and * to add the element to all the subplots in the current nesting level

(mean_age_kps_line / (wt_ht_scatter | hist_age)) * theme_minimal()

Controlling guides

If multiple plots have same guide, using guides = "collect" in plot_layout() drops the duplicate guide

(groupedbar_gender | (stackedbar_gender / hist_age)) +
  plot_layout(guides = "collect")